[FieldTrip] common filter with more than two conditions

Lin Wang wanglinsisi at gmail.com
Tue Mar 8 01:16:27 CET 2016

Hi Arjen,

Yes, it answers my question. Thank! :-)

Then a further question is: is it better to include as many trials as
possible to have a good estimation of the covariance matrix (provided that
the signals are good for all trials)?

For example, there are two experimental conditions, with 20 trials per
condition. There are also 40 filler trials with a similar structure as the
experimental conditions. In this case, can I combine all the conditions to
build the common filter and then only compare the two experimental
conditions later?

The cognitive processes might be different between the experimental
conditions and the fillers, so I'm not sure whether combining them has any
influence on the spatial filter.


On Tue, Mar 8, 2016 at 12:19 AM, Arjen Stolk <a.stolk8 at gmail.com> wrote:

> Hey Lin,
> Provided that there are no systematic confounds (e.g. head position)
> across conditions, you could construct a common filter based on data from
> all conditions. I would leave any statistical comparison to after
> source-reconstruction.
> Does that answer your question?
> Arjen
> 2016-03-07 1:51 GMT-08:00 Lin Wang <wanglinsisi at gmail.com>:
>> Dear community,
>> I'm trying to do lcmv beamformer source analysis with a common filter for
>> more than two conditions. I have a 2A (A1, A2) * 2B (B1,B2) design, and I
>> am interested in both the main effect of A (A1 vs. A2) as well as the
>> simple effects (A1B1 vs. A2B1 and A1B2 vs. A2B2).
>> My question is how to build the common filter. I could combine all the
>> four conditions to obtain a common filter for the contrast of A1 vs. A2.
>> Then can I also use this common filter to compare A1B1 vs. A2B1? Or do I
>> have to build a different common filter (to combine the A1B1 and A2B1
>> conditions) for the contrast of A1B1 vs. A2B1?
>> Thanks for your help in advance!
>> Best,
>> Lin
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